Relative Contributions of Internal and External Features to Face Recognition

The central challenge in face recognition lies in understanding the role different facial features play in our judgments of identity. Notable in this regard are the relative contributions of the internal (eyes, nose and mouth) and external (hair and jaw-line) features. Past studies that have investigated this issue have typically used high-resolution images or good-quality line drawings as facial stimuli. The results obtained are therefore most relevant for understanding the identification of faces at close range. However, given that real-world viewing conditions are rarely optimal, it is also important to know how image degradations, such as loss of resolution caused by large viewing distances, influence our ability to use internal and external features. Here, we report experiments designed to address this issue. Our data characterize how the relative contributions of internal and external features change as a function of image resolution. While we replicated results of previous studies that have shown internal features of familiar faces to be more useful for recognition than external features at high resolution, we found that the two feature sets reverse in importance as resolution decreases. These results suggest that the visual system uses a highly non-linear cue-fusion strategy in combining internal and external features along the dimension of image resolution and that the configural cues that relate the two feature sets play an important role in judgments of facial identity.

Relative Affine Structure: Canonical Model for 3D from 2D Geometry and Applications

We propose an affine framework for perspective views, captured by a single extremely simple equation based on a viewer-centered invariant we call "relative affine structure". Via a number of corollaries of our main results we show that our framework unifies previous work --- including Euclidean, projective and affine --- in a natural and simple way, and introduces new, extremely simple, algorithms for the tasks of reconstruction from multiple views, recognition by alignment, and certain image coding applications.

Colliding and Moving Bose-Einstein Condensates: Studies of superfluidity and optical tweezers for condensate transport

In this thesis, two different sets of experiments are described. The first is an exploration of the microscopic superfluidity of dilute gaseous Bose- Einstein condensates. The second set of experiments were performed using transported condensates in a new BEC apparatus. Superfluidity was probed by moving impurities through a trapped condensate. The impurities were created using an optical Raman transition, which transferred a small fraction of the atoms into an untrapped hyperfine state. A dramatic reduction in the collisions between the moving impurities and the condensate was observed when the velocity of the impurities was close to the speed of sound of the condensate. This reduction was attributed to the superfluid properties of a BEC. In addition, we observed an increase in the collisional density as the number of impurity atoms increased. This enhancement is an indication of bosonic stimulation by the occupied final states. This stimulation was observed both at small and large velocities relative to the speed of sound. A theoretical calculation of the effect of finite temperature indicated that collision rate should be enhanced at small velocities due to thermal excitations. However, in the current experiments we were insensitive to this effect. Finally, the factor of two between the collisional rate between indistinguishable and distinguishable atoms was confirmed. A new BEC apparatus that can transport condensates using optical tweezers was constructed. Condensates containing 10-15 million sodium atoms were produced in 20 s using conventional BEC production techniques. These condensates were then transferred into an optical trap that was translated from the ‘production chamber’ into a separate vacuum chamber: the ‘science chamber’. Typically, we transferred 2-3 million condensed atoms in less than 2 s. This transport technique avoids optical and mechanical constrainsts of conventional condensate experiments and allows for the possibility of novel experiments. In the first experiments using transported BEC, we loaded condensed atoms from the optical tweezers into both macroscopic and miniaturized magnetic traps. Using microfabricated wires on a silicon chip, we observed excitation-less propagation of a BEC in a magnetic waveguide. The condensates fragmented when brought very close to the wire surface indicating that imperfections in the fabrication process might limit future experiments. Finally, we generated a continuous BEC source by periodically replenishing a condensate held in an optical reservoir trap using fresh condensates delivered using optical tweezers. More than a million condensed atoms were always present in the continuous source, raising the possibility of realizing a truly continuous atom lase.

From First Contact to Close Encounters: A Developmentally Deep Perceptual System for a Humanoid Robot

This thesis presents a perceptual system for a humanoid robot that integrates abilities such as object localization and recognition with the deeper developmental machinery required to forge those competences out of raw physical experiences. It shows that a robotic platform can build up and maintain a system for object localization, segmentation, and recognition, starting from very little. What the robot starts with is a direct solution to achieving figure/ground separation: it simply 'pokes around' in a region of visual ambiguity and watches what happens. If the arm passes through an area, that area is recognized as free space. If the arm collides with an object, causing it to move, the robot can use that motion to segment the object from the background. Once the robot can acquire reliable segmented views of objects, it learns from them, and from then on recognizes and segments those objects without further contact. Both low-level and high-level visual features can also be learned in this way, and examples are presented for both: orientation detection and affordance recognition, respectively. The motivation for this work is simple. Training on large corpora of annotated real-world data has proven crucial for creating robust solutions to perceptual problems such as speech recognition and face detection. But the powerful tools used during training of such systems are typically stripped away at deployment. Ideally they should remain, particularly for unstable tasks such as object detection, where the set of objects needed in a task tomorrow might be different from the set of objects needed today. The key limiting factor is access to training data, but as this thesis shows, that need not be a problem on a robotic platform that can actively probe its environment, and carry out experiments to resolve ambiguity. This work is an instance of a general approach to learning a new perceptual judgment: find special situations in which the perceptual judgment is easy and study these situations to find correlated features that can be observed more generally.